Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations4484
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory569.3 B

Variable types

Text4
Numeric13
Categorical3

Alerts

52 Weeks High is highly overall correlated with 52 Weeks Low and 6 other fieldsHigh correlation
52 Weeks Low is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Currency is highly overall correlated with EPS AnnualHigh correlation
EPS Annual is highly overall correlated with 52 Weeks High and 7 other fieldsHigh correlation
Market Cap (in M) is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
Performance (52 weeks) is highly overall correlated with Market Cap (in M) and 1 other fieldsHigh correlation
Price is highly overall correlated with 52 Weeks High and 7 other fieldsHigh correlation
Price 52 Weeks Ago is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
ROI Annual is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Résultat net is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Total assets is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Volume 1 month is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Volume 52 weeks is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Currency is highly imbalanced (92.2%)Imbalance
Price is highly skewed (γ1 = 26.65821918)Skewed
Market Cap (in M) is highly skewed (γ1 = 24.19578148)Skewed
Volume 52 weeks is highly skewed (γ1 = 42.92841481)Skewed
Volume 1 month is highly skewed (γ1 = 27.43232027)Skewed
52 Weeks High is highly skewed (γ1 = 33.85126792)Skewed
52 Weeks Low is highly skewed (γ1 = 22.9559375)Skewed
Price 52 Weeks Ago is highly skewed (γ1 = 33.57671223)Skewed
EPS Annual is highly skewed (γ1 = 47.96902073)Skewed
ROI Annual is highly skewed (γ1 = -51.21208072)Skewed
Symbol has unique valuesUnique
Company Name has unique valuesUnique
Market Cap (in M) has unique valuesUnique
Beta has unique valuesUnique

Reproduction

Analysis started2024-08-12 18:43:11.074302
Analysis finished2024-08-12 18:43:28.213421
Duration17.14 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Symbol
Text

UNIQUE 

Distinct4484
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size265.6 KiB
2024-08-12T20:43:28.453789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.6291258
Min length1

Characters and Unicode

Total characters16273
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4484 ?
Unique (%)100.0%

Sample

1st rowTRNS
2nd rowACRV
3rd rowCOLM
4th rowMOVE
5th rowHCKT
ValueCountFrequency (%)
trns 1
 
< 0.1%
itri 1
 
< 0.1%
move 1
 
< 0.1%
hckt 1
 
< 0.1%
hcwb 1
 
< 0.1%
meip 1
 
< 0.1%
lfus 1
 
< 0.1%
govx 1
 
< 0.1%
cndt 1
 
< 0.1%
hqy 1
 
< 0.1%
Other values (4474) 4474
99.8%
2024-08-12T20:43:28.853487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1166
 
7.2%
C 1150
 
7.1%
T 1098
 
6.7%
S 1082
 
6.6%
R 1073
 
6.6%
N 941
 
5.8%
L 836
 
5.1%
I 827
 
5.1%
M 743
 
4.6%
E 741
 
4.6%
Other values (16) 6616
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1166
 
7.2%
C 1150
 
7.1%
T 1098
 
6.7%
S 1082
 
6.6%
R 1073
 
6.6%
N 941
 
5.8%
L 836
 
5.1%
I 827
 
5.1%
M 743
 
4.6%
E 741
 
4.6%
Other values (16) 6616
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1166
 
7.2%
C 1150
 
7.1%
T 1098
 
6.7%
S 1082
 
6.6%
R 1073
 
6.6%
N 941
 
5.8%
L 836
 
5.1%
I 827
 
5.1%
M 743
 
4.6%
E 741
 
4.6%
Other values (16) 6616
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1166
 
7.2%
C 1150
 
7.1%
T 1098
 
6.7%
S 1082
 
6.6%
R 1073
 
6.6%
N 941
 
5.8%
L 836
 
5.1%
I 827
 
5.1%
M 743
 
4.6%
E 741
 
4.6%
Other values (16) 6616
40.7%

Company Name
Text

UNIQUE 

Distinct4484
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
2024-08-12T20:43:29.069591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length55
Median length39
Mean length19.652319
Min length2

Characters and Unicode

Total characters88121
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4484 ?
Unique (%)100.0%

Sample

1st rowTranscat Inc
2nd rowAcrivon Therapeutics Inc
3rd rowColumbia Sportswear Co
4th rowMovano Inc
5th rowHackett Group Inc
ValueCountFrequency (%)
inc 2844
 
21.0%
corp 824
 
6.1%
ltd 337
 
2.5%
holdings 333
 
2.5%
group 250
 
1.8%
co 185
 
1.4%
therapeutics 174
 
1.3%
financial 114
 
0.8%
technologies 110
 
0.8%
bancorp 97
 
0.7%
Other values (4774) 8299
61.2%
2024-08-12T20:43:29.425951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9083
 
10.3%
n 7523
 
8.5%
e 5933
 
6.7%
o 5594
 
6.3%
c 5342
 
6.1%
r 5179
 
5.9%
i 4996
 
5.7%
a 4912
 
5.6%
t 4048
 
4.6%
s 3550
 
4.0%
Other values (64) 31961
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 88121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9083
 
10.3%
n 7523
 
8.5%
e 5933
 
6.7%
o 5594
 
6.3%
c 5342
 
6.1%
r 5179
 
5.9%
i 4996
 
5.7%
a 4912
 
5.6%
t 4048
 
4.6%
s 3550
 
4.0%
Other values (64) 31961
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 88121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9083
 
10.3%
n 7523
 
8.5%
e 5933
 
6.7%
o 5594
 
6.3%
c 5342
 
6.1%
r 5179
 
5.9%
i 4996
 
5.7%
a 4912
 
5.6%
t 4048
 
4.6%
s 3550
 
4.0%
Other values (64) 31961
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 88121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9083
 
10.3%
n 7523
 
8.5%
e 5933
 
6.7%
o 5594
 
6.3%
c 5342
 
6.1%
r 5179
 
5.9%
i 4996
 
5.7%
a 4912
 
5.6%
t 4048
 
4.6%
s 3550
 
4.0%
Other values (64) 31961
36.3%

Price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3345
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.376001
Minimum0.0503
Maximum8506.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 KiB
2024-08-12T20:43:29.553223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0503
5-th percentile0.63
Q13.4
median13.19
Q344.92
95-th percentile200.8075
Maximum8506.24
Range8506.1897
Interquartile range (IQR)41.52

Descriptive statistics

Standard deviation181.92468
Coefficient of variation (CV)3.5410439
Kurtosis1096.9649
Mean51.376001
Median Absolute Deviation (MAD)11.804
Skewness26.658219
Sum230369.99
Variance33096.588
MonotonicityNot monotonic
2024-08-12T20:43:29.664577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.01 8
 
0.2%
1.06 8
 
0.2%
1.47 7
 
0.2%
1.4 7
 
0.2%
1.65 7
 
0.2%
1.02 7
 
0.2%
1.6 7
 
0.2%
11.45 7
 
0.2%
1.27 6
 
0.1%
1.16 6
 
0.1%
Other values (3335) 4414
98.4%
ValueCountFrequency (%)
0.0503 1
< 0.1%
0.075 1
< 0.1%
0.076 1
< 0.1%
0.0766 1
< 0.1%
0.08 1
< 0.1%
0.0823 1
< 0.1%
0.092 1
< 0.1%
0.0933 1
< 0.1%
0.0945 1
< 0.1%
0.0978 1
< 0.1%
ValueCountFrequency (%)
8506.24 1
< 0.1%
3443.05 1
< 0.1%
3120.25 1
< 0.1%
1974.15 1
< 0.1%
1883.62 1
< 0.1%
1752.25 1
< 0.1%
1701.48 1
< 0.1%
1521.92 1
< 0.1%
1397.26 1
< 0.1%
1259.41 1
< 0.1%

Market Cap (in M)
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct4484
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12557.237
Minimum0.20246208
Maximum3287742.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 KiB
2024-08-12T20:43:29.780136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.20246208
5-th percentile6.508354
Q195.831665
median706.08459
Q34136.5028
95-th percentile42597.148
Maximum3287742.5
Range3287742.3
Interquartile range (IQR)4040.6711

Descriptive statistics

Standard deviation96488.719
Coefficient of variation (CV)7.6839129
Kurtosis692.87098
Mean12557.237
Median Absolute Deviation (MAD)690.17186
Skewness24.195781
Sum56306653
Variance9.3100729 × 109
MonotonicityNot monotonic
2024-08-12T20:43:29.897085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1051.552692 1
 
< 0.1%
3556.372052 1
 
< 0.1%
131.910937 1
 
< 0.1%
11157.58663 1
 
< 0.1%
38901.79531 1
 
< 0.1%
4240.310905 1
 
< 0.1%
25967.83614 1
 
< 0.1%
4306.491217 1
 
< 0.1%
11553.67616 1
 
< 0.1%
4822.75805 1
 
< 0.1%
Other values (4474) 4474
99.8%
ValueCountFrequency (%)
0.202462083 1
< 0.1%
0.290874472 1
< 0.1%
0.3662907246 1
< 0.1%
0.5117001326 1
< 0.1%
0.6849636886 1
< 0.1%
0.6859719465 1
< 0.1%
0.727851 1
< 0.1%
0.7453370722 1
< 0.1%
0.7492588667 1
< 0.1%
0.7598715686 1
< 0.1%
ValueCountFrequency (%)
3287742.486 1
< 0.1%
3017962.34 1
< 0.1%
2581647.096 1
< 0.1%
2024988.839 1
< 0.1%
1752130.051 1
< 0.1%
1309863.215 1
< 0.1%
847457.4806 1
< 0.1%
690133.0242 1
< 0.1%
638928.0644 1
< 0.1%
585534.9078 1
< 0.1%

Beta
Real number (ℝ)

UNIQUE 

Distinct4484
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0681307
Minimum-6.9775743
Maximum19.570795
Zeros0
Zeros (%)0.0%
Negative423
Negative (%)9.4%
Memory size35.2 KiB
2024-08-12T20:43:30.123313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.9775743
5-th percentile-0.22385404
Q10.44176187
median0.93722856
Q31.5533651
95-th percentile2.8681994
Maximum19.570795
Range26.548369
Interquartile range (IQR)1.1116033

Descriptive statistics

Standard deviation1.0770051
Coefficient of variation (CV)1.0083083
Kurtosis25.592616
Mean1.0681307
Median Absolute Deviation (MAD)0.5464041
Skewness1.9498083
Sum4789.4982
Variance1.1599399
MonotonicityNot monotonic
2024-08-12T20:43:30.232797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9319894 1
 
< 0.1%
2.585754 1
 
< 0.1%
-0.0058279326 1
 
< 0.1%
0.7019731 1
 
< 0.1%
0.46431598 1
 
< 0.1%
2.0908768 1
 
< 0.1%
1.1370689 1
 
< 0.1%
1.1250257 1
 
< 0.1%
1.0901773 1
 
< 0.1%
1.2514409 1
 
< 0.1%
Other values (4474) 4474
99.8%
ValueCountFrequency (%)
-6.9775743 1
< 0.1%
-4.9759016 1
< 0.1%
-4.3898983 1
< 0.1%
-3.8894632 1
< 0.1%
-3.6600218 1
< 0.1%
-3.375102 1
< 0.1%
-3.0445251 1
< 0.1%
-3.043933 1
< 0.1%
-2.8673244 1
< 0.1%
-2.8665426 1
< 0.1%
ValueCountFrequency (%)
19.570795 1
< 0.1%
11.313087 1
< 0.1%
9.652831 1
< 0.1%
8.4484005 1
< 0.1%
8.446643 1
< 0.1%
7.075054 1
< 0.1%
7.0186133 1
< 0.1%
6.7305875 1
< 0.1%
6.5846024 1
< 0.1%
5.979147 1
< 0.1%

Volume 52 weeks
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4482
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1553905.8
Minimum849.20635
Maximum4.6049593 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 KiB
2024-08-12T20:43:30.337991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum849.20635
5-th percentile12684.107
Q1104548.02
median419818.29
Q31222540.1
95-th percentile5539635.7
Maximum4.6049593 × 108
Range4.6049508 × 108
Interquartile range (IQR)1117992.1

Descriptive statistics

Standard deviation8039876.7
Coefficient of variation (CV)5.1739796
Kurtosis2380.2548
Mean1553905.8
Median Absolute Deviation (MAD)371740.87
Skewness42.928415
Sum6.9677134 × 109
Variance6.4639617 × 1013
MonotonicityNot monotonic
2024-08-12T20:43:30.443368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225453.9683 2
 
< 0.1%
6648.015873 2
 
< 0.1%
445934.9206 1
 
< 0.1%
1866615.476 1
 
< 0.1%
487720.6349 1
 
< 0.1%
74468.25397 1
 
< 0.1%
192685.3175 1
 
< 0.1%
15696701.59 1
 
< 0.1%
1345657.143 1
 
< 0.1%
2096362.698 1
 
< 0.1%
Other values (4472) 4472
99.7%
ValueCountFrequency (%)
849.2063492 1
< 0.1%
935.059761 1
< 0.1%
1153.174603 1
< 0.1%
1188.095238 1
< 0.1%
1755.952381 1
< 0.1%
1777.777778 1
< 0.1%
1797.619048 1
< 0.1%
2017.063492 1
< 0.1%
2108.333333 1
< 0.1%
2126.587302 1
< 0.1%
ValueCountFrequency (%)
460495926.5 1
< 0.1%
107475531.3 1
< 0.1%
60843084.92 1
< 0.1%
60546926.98 1
< 0.1%
56703451.59 1
< 0.1%
53449109.52 1
< 0.1%
53207215.08 1
< 0.1%
52205071.83 1
< 0.1%
49901964.06 1
< 0.1%
46498527.38 1
< 0.1%

Volume 1 month
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4427
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1730573.3
Minimum65.217391
Maximum3.4773365 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 KiB
2024-08-12T20:43:30.549293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum65.217391
5-th percentile9101.9565
Q192364.13
median428658.7
Q31320712
95-th percentile6697659.3
Maximum3.4773365 × 108
Range3.4773358 × 108
Interquartile range (IQR)1228347.8

Descriptive statistics

Standard deviation7285038.7
Coefficient of variation (CV)4.2096101
Kurtosis1170.221
Mean1730573.3
Median Absolute Deviation (MAD)394232.24
Skewness27.43232
Sum7.7598905 × 109
Variance5.3071789 × 1013
MonotonicityNot monotonic
2024-08-12T20:43:30.655833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52026.08696 3
 
0.1%
81686.95652 3
 
0.1%
5908.695652 2
 
< 0.1%
7100 2
 
< 0.1%
29304.34783 2
 
< 0.1%
839.1304348 2
 
< 0.1%
83213.04348 2
 
< 0.1%
55626.08696 2
 
< 0.1%
54586.95652 2
 
< 0.1%
1556.521739 2
 
< 0.1%
Other values (4417) 4462
99.5%
ValueCountFrequency (%)
65.2173913 1
< 0.1%
73.91304348 1
< 0.1%
139.1304348 1
< 0.1%
181.8181818 1
< 0.1%
182.6086957 1
< 0.1%
213.0434783 1
< 0.1%
217.3913043 2
< 0.1%
362.3043478 1
< 0.1%
468.1818182 1
< 0.1%
500 1
< 0.1%
ValueCountFrequency (%)
347733646.8 1
< 0.1%
110366273.9 1
< 0.1%
108802565.2 1
< 0.1%
81539721.74 1
< 0.1%
78406543.48 1
< 0.1%
63759239.13 1
< 0.1%
60902782.61 1
< 0.1%
59622121.74 1
< 0.1%
58253669.57 1
< 0.1%
53438513.04 1
< 0.1%

52 Weeks High
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3519
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.432639
Minimum0.87
Maximum14400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 KiB
2024-08-12T20:43:30.768492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.87
5-th percentile2.36
Q18.7
median21.812
Q361.0275
95-th percentile247.465
Maximum14400
Range14399.13
Interquartile range (IQR)52.3275

Descriptive statistics

Standard deviation291.28036
Coefficient of variation (CV)4.2564537
Kurtosis1492.6248
Mean68.432639
Median Absolute Deviation (MAD)16.9765
Skewness33.851268
Sum306851.95
Variance84844.249
MonotonicityNot monotonic
2024-08-12T20:43:30.871208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.05 8
 
0.2%
13 8
 
0.2%
14 7
 
0.2%
2.1 7
 
0.2%
3.5 6
 
0.1%
3.6 6
 
0.1%
13.5 5
 
0.1%
9.2 5
 
0.1%
11.59 5
 
0.1%
6.3 5
 
0.1%
Other values (3509) 4422
98.6%
ValueCountFrequency (%)
0.87 1
< 0.1%
0.8999 2
< 0.1%
0.9039 1
< 0.1%
0.909 1
< 0.1%
0.93 1
< 0.1%
0.94 1
< 0.1%
0.98 2
< 0.1%
0.99 1
< 0.1%
1.01 1
< 0.1%
1.02 1
< 0.1%
ValueCountFrequency (%)
14400 1
< 0.1%
8700 1
< 0.1%
4144.32 1
< 0.1%
3242.54 1
< 0.1%
2173.01 1
< 0.1%
1905.09 1
< 0.1%
1899.21 1
< 0.1%
1759.76 1
< 0.1%
1670.24 1
< 0.1%
1535.86 1
< 0.1%

52 Weeks Low
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3292
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.570781
Minimum0.0004
Maximum5210.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 KiB
2024-08-12T20:43:30.979215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0004
5-th percentile0.445815
Q12.2
median10.3
Q333.04625
95-th percentile147.59
Maximum5210.49
Range5210.4896
Interquartile range (IQR)30.84625

Descriptive statistics

Standard deviation121.79573
Coefficient of variation (CV)3.3304109
Kurtosis822.72228
Mean36.570781
Median Absolute Deviation (MAD)9.2
Skewness22.955937
Sum163983.38
Variance14834.199
MonotonicityNot monotonic
2024-08-12T20:43:31.089338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75 11
 
0.2%
0.7 9
 
0.2%
1 9
 
0.2%
1.21 9
 
0.2%
1.04 9
 
0.2%
0.65 9
 
0.2%
1.25 8
 
0.2%
1.37 8
 
0.2%
1.1 8
 
0.2%
1.55 8
 
0.2%
Other values (3282) 4396
98.0%
ValueCountFrequency (%)
0.0004 1
< 0.1%
0.038 1
< 0.1%
0.048 1
< 0.1%
0.056 1
< 0.1%
0.064 1
< 0.1%
0.07 1
< 0.1%
0.0712 1
< 0.1%
0.0734 1
< 0.1%
0.0771 1
< 0.1%
0.0811 1
< 0.1%
ValueCountFrequency (%)
5210.49 1
< 0.1%
2735.3 1
< 0.1%
2379.02 1
< 0.1%
1401.0101 1
< 0.1%
1295.65 1
< 0.1%
1274.91 1
< 0.1%
1141.04 1
< 0.1%
930.72 1
< 0.1%
860.1 1
< 0.1%
811.99 1
< 0.1%

Exchange
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size272.8 KiB
NASDAQ
2849 
NYSE
1635 

Length

Max length6
Median length6
Mean length5.2707404
Min length4

Characters and Unicode

Total characters23634
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNASDAQ
2nd rowNASDAQ
3rd rowNASDAQ
4th rowNASDAQ
5th rowNASDAQ

Common Values

ValueCountFrequency (%)
NASDAQ 2849
63.5%
NYSE 1635
36.5%

Length

2024-08-12T20:43:31.190522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T20:43:31.294327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nasdaq 2849
63.5%
nyse 1635
36.5%

Most occurring characters

ValueCountFrequency (%)
A 5698
24.1%
N 4484
19.0%
S 4484
19.0%
D 2849
12.1%
Q 2849
12.1%
Y 1635
 
6.9%
E 1635
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5698
24.1%
N 4484
19.0%
S 4484
19.0%
D 2849
12.1%
Q 2849
12.1%
Y 1635
 
6.9%
E 1635
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5698
24.1%
N 4484
19.0%
S 4484
19.0%
D 2849
12.1%
Q 2849
12.1%
Y 1635
 
6.9%
E 1635
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5698
24.1%
N 4484
19.0%
S 4484
19.0%
D 2849
12.1%
Q 2849
12.1%
Y 1635
 
6.9%
E 1635
 
6.9%

Performance (52 weeks)
Real number (ℝ)

HIGH CORRELATION 

Distinct4477
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0094150461
Minimum-0.99985424
Maximum33.280986
Zeros0
Zeros (%)0.0%
Negative2323
Negative (%)51.8%
Memory size35.2 KiB
2024-08-12T20:43:31.384157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.99985424
5-th percentile-0.85645913
Q1-0.36256505
median-0.018677708
Q30.22021245
95-th percentile0.79497716
Maximum33.280986
Range34.28084
Interquartile range (IQR)0.5827775

Descriptive statistics

Standard deviation0.7969889
Coefficient of variation (CV)-84.650557
Kurtosis690.48295
Mean-0.0094150461
Median Absolute Deviation (MAD)0.28149938
Skewness17.975079
Sum-42.217067
Variance0.63519131
MonotonicityNot monotonic
2024-08-12T20:43:31.487564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8042474026 2
 
< 0.1%
-0.47592853 2
 
< 0.1%
0.387608379 2
 
< 0.1%
-0.06056350255 2
 
< 0.1%
-0.5009512193 2
 
< 0.1%
0.07029265643 2
 
< 0.1%
-0.1670839982 2
 
< 0.1%
0.2815539887 1
 
< 0.1%
-0.6962634858 1
 
< 0.1%
-0.2689523163 1
 
< 0.1%
Other values (4467) 4467
99.6%
ValueCountFrequency (%)
-0.9998542374 1
< 0.1%
-0.9994216567 1
< 0.1%
-0.9992965079 1
< 0.1%
-0.9982727312 1
< 0.1%
-0.9979352051 1
< 0.1%
-0.9973795645 1
< 0.1%
-0.9973444564 1
< 0.1%
-0.9965653988 1
< 0.1%
-0.9964575223 1
< 0.1%
-0.9963780463 1
< 0.1%
ValueCountFrequency (%)
33.28098556 1
< 0.1%
8.725687571 1
< 0.1%
7.564530876 1
< 0.1%
7.492914449 1
< 0.1%
6.921500924 1
< 0.1%
5.897939474 1
< 0.1%
5.736607268 1
< 0.1%
5.64065769 1
< 0.1%
5.450054284 1
< 0.1%
5.299123385 1
< 0.1%
Distinct51
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size258.5 KiB
2024-08-12T20:43:31.608212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8968
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.3%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 3820
85.2%
cn 169
 
3.8%
il 78
 
1.7%
gb 56
 
1.2%
ca 53
 
1.2%
sg 34
 
0.8%
hk 32
 
0.7%
ie 29
 
0.6%
bm 29
 
0.6%
ky 18
 
0.4%
Other values (41) 166
 
3.7%
2024-08-12T20:43:31.913270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 3862
43.1%
U 3843
42.9%
C 250
 
2.8%
N 183
 
2.0%
I 116
 
1.3%
G 108
 
1.2%
L 101
 
1.1%
B 97
 
1.1%
A 67
 
0.7%
K 57
 
0.6%
Other values (15) 284
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 3862
43.1%
U 3843
42.9%
C 250
 
2.8%
N 183
 
2.0%
I 116
 
1.3%
G 108
 
1.2%
L 101
 
1.1%
B 97
 
1.1%
A 67
 
0.7%
K 57
 
0.6%
Other values (15) 284
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 3862
43.1%
U 3843
42.9%
C 250
 
2.8%
N 183
 
2.0%
I 116
 
1.3%
G 108
 
1.2%
L 101
 
1.1%
B 97
 
1.1%
A 67
 
0.7%
K 57
 
0.6%
Other values (15) 284
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 3862
43.1%
U 3843
42.9%
C 250
 
2.8%
N 183
 
2.0%
I 116
 
1.3%
G 108
 
1.2%
L 101
 
1.1%
B 97
 
1.1%
A 67
 
0.7%
K 57
 
0.6%
Other values (15) 284
 
3.2%

Résultat net
Real number (ℝ)

HIGH CORRELATION 

Distinct4449
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.728067 × 108
Minimum-2.1601255 × 1010
Maximum1.2241905 × 1011
Zeros0
Zeros (%)0.0%
Negative2106
Negative (%)47.0%
Memory size35.2 KiB
2024-08-12T20:43:32.019469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.1601255 × 1010
5-th percentile-2.8764924 × 108
Q1-36808000
median2761843
Q31.4677326 × 108
95-th percentile1.9950549 × 109
Maximum1.2241905 × 1011
Range1.4402031 × 1011
Interquartile range (IQR)1.8358126 × 108

Descriptive statistics

Standard deviation4.0510302 × 109
Coefficient of variation (CV)8.5680473
Kurtosis450.73695
Mean4.728067 × 108
Median Absolute Deviation (MAD)68221500
Skewness19.267203
Sum2.1200653 × 1012
Variance1.6410846 × 1019
MonotonicityNot monotonic
2024-08-12T20:43:32.122935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1052000000 3
 
0.1%
714000000 3
 
0.1%
290000000 2
 
< 0.1%
369564992 2
 
< 0.1%
-7000000 2
 
< 0.1%
-35390000 2
 
< 0.1%
-40105000 2
 
< 0.1%
2560000000 2
 
< 0.1%
36000000 2
 
< 0.1%
-120737000 2
 
< 0.1%
Other values (4439) 4462
99.5%
ValueCountFrequency (%)
-2.160125542 × 10101
< 0.1%
-1.176899994 × 10101
< 0.1%
-9406706688 1
< 0.1%
-6808999936 1
< 0.1%
-6541000192 1
< 0.1%
-5866999808 1
< 0.1%
-5810999808 1
< 0.1%
-5791000064 1
< 0.1%
-5783000064 1
< 0.1%
-4943179776 1
< 0.1%
ValueCountFrequency (%)
1.224190525 × 10111
< 0.1%
1.019560018 × 10111
< 0.1%
8.813599949 × 10101
< 0.1%
8.765699686 × 10101
< 0.1%
7.992333926 × 10101
< 0.1%
7.974100173 × 10101
< 0.1%
5.221699994 × 10101
< 0.1%
5.143400038 × 10101
< 0.1%
4.441899827 × 10101
< 0.1%
4.259799859 × 10101
< 0.1%

Sector
Categorical

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size307.3 KiB
Healthcare
992 
Financial Services
741 
Technology
672 
Industrials
536 
Consumer Cyclical
494 
Other values (6)
1049 

Length

Max length22
Median length18
Mean length13.149866
Min length6

Characters and Unicode

Total characters58964
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndustrials
2nd rowHealthcare
3rd rowConsumer Cyclical
4th rowHealthcare
5th rowTechnology

Common Values

ValueCountFrequency (%)
Healthcare 992
22.1%
Financial Services 741
16.5%
Technology 672
15.0%
Industrials 536
12.0%
Consumer Cyclical 494
11.0%
Real Estate 227
 
5.1%
Consumer Defensive 206
 
4.6%
Communication Services 195
 
4.3%
Energy 178
 
4.0%
Basic Materials 157
 
3.5%

Length

2024-08-12T20:43:32.231175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
healthcare 992
15.3%
services 936
14.4%
financial 741
11.4%
consumer 700
10.8%
technology 672
10.3%
industrials 536
8.2%
cyclical 494
7.6%
real 227
 
3.5%
estate 227
 
3.5%
defensive 206
 
3.2%
Other values (5) 773
11.9%

Most occurring characters

ValueCountFrequency (%)
e 6721
11.4%
a 5616
 
9.5%
c 4681
 
7.9%
i 4616
 
7.8%
l 4399
 
7.5%
n 4164
 
7.1%
s 3541
 
6.0%
r 3499
 
5.9%
t 2506
 
4.3%
o 2434
 
4.1%
Other values (21) 16787
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58964
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6721
11.4%
a 5616
 
9.5%
c 4681
 
7.9%
i 4616
 
7.8%
l 4399
 
7.5%
n 4164
 
7.1%
s 3541
 
6.0%
r 3499
 
5.9%
t 2506
 
4.3%
o 2434
 
4.1%
Other values (21) 16787
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58964
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6721
11.4%
a 5616
 
9.5%
c 4681
 
7.9%
i 4616
 
7.8%
l 4399
 
7.5%
n 4164
 
7.1%
s 3541
 
6.0%
r 3499
 
5.9%
t 2506
 
4.3%
o 2434
 
4.1%
Other values (21) 16787
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58964
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6721
11.4%
a 5616
 
9.5%
c 4681
 
7.9%
i 4616
 
7.8%
l 4399
 
7.5%
n 4164
 
7.1%
s 3541
 
6.0%
r 3499
 
5.9%
t 2506
 
4.3%
o 2434
 
4.1%
Other values (21) 16787
28.5%
Distinct144
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size335.3 KiB
2024-08-12T20:43:32.414582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length32
Mean length19.541035
Min length4

Characters and Unicode

Total characters87622
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIndustrial Distribution
2nd rowBiotechnology
3rd rowApparel Manufacturing
4th rowMedical Devices
5th rowInformation Technology Services
ValueCountFrequency (%)
2207
 
18.9%
biotechnology 561
 
4.8%
services 428
 
3.7%
software 342
 
2.9%
banks 294
 
2.5%
specialty 290
 
2.5%
regional 289
 
2.5%
medical 232
 
2.0%
application 204
 
1.7%
equipment 194
 
1.7%
Other values (189) 6632
56.8%
2024-08-12T20:43:32.725770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8295
 
9.5%
7189
 
8.2%
i 6620
 
7.6%
t 5887
 
6.7%
a 5741
 
6.6%
n 5676
 
6.5%
o 5137
 
5.9%
r 4470
 
5.1%
s 4330
 
4.9%
c 4122
 
4.7%
Other values (38) 30155
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8295
 
9.5%
7189
 
8.2%
i 6620
 
7.6%
t 5887
 
6.7%
a 5741
 
6.6%
n 5676
 
6.5%
o 5137
 
5.9%
r 4470
 
5.1%
s 4330
 
4.9%
c 4122
 
4.7%
Other values (38) 30155
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8295
 
9.5%
7189
 
8.2%
i 6620
 
7.6%
t 5887
 
6.7%
a 5741
 
6.6%
n 5676
 
6.5%
o 5137
 
5.9%
r 4470
 
5.1%
s 4330
 
4.9%
c 4122
 
4.7%
Other values (38) 30155
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8295
 
9.5%
7189
 
8.2%
i 6620
 
7.6%
t 5887
 
6.7%
a 5741
 
6.6%
n 5676
 
6.5%
o 5137
 
5.9%
r 4470
 
5.1%
s 4330
 
4.9%
c 4122
 
4.7%
Other values (38) 30155
34.4%

Price 52 Weeks Ago
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3595
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.95327
Minimum0.30000001
Maximum11250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 KiB
2024-08-12T20:43:32.842404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.30000001
5-th percentile1.46
Q16.0075002
median15.371808
Q344.968674
95-th percentile182.8655
Maximum11250
Range11249.7
Interquartile range (IQR)38.961174

Descriptive statistics

Standard deviation226.19774
Coefficient of variation (CV)4.4393175
Kurtosis1482.6072
Mean50.95327
Median Absolute Deviation (MAD)12.391808
Skewness33.576712
Sum228474.46
Variance51165.418
MonotonicityNot monotonic
2024-08-12T20:43:32.943475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.220000029 8
 
0.2%
10.69999981 8
 
0.2%
2.049999952 8
 
0.2%
3.019999981 7
 
0.2%
1.679999948 7
 
0.2%
4.199999809 7
 
0.2%
12 7
 
0.2%
3.779999971 6
 
0.1%
2.559999943 6
 
0.1%
7.400000095 6
 
0.1%
Other values (3585) 4414
98.4%
ValueCountFrequency (%)
0.3000000119 1
< 0.1%
0.400000006 1
< 0.1%
0.4300000072 1
< 0.1%
0.4379999936 1
< 0.1%
0.4799999893 1
< 0.1%
0.5009999871 1
< 0.1%
0.5099999905 1
< 0.1%
0.5170000196 1
< 0.1%
0.5210062861 1
< 0.1%
0.5320000052 1
< 0.1%
ValueCountFrequency (%)
11250 1
< 0.1%
6156.72998 1
< 0.1%
3456 1
< 0.1%
3190.70166 1
< 0.1%
2483.830078 1
< 0.1%
1566.987183 1
< 0.1%
1506.199951 1
< 0.1%
1464.240601 1
< 0.1%
1330 1
< 0.1%
1239.800049 1
< 0.1%

Currency
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size262.9 KiB
USD
4309 
CNY
 
92
EUR
 
27
CAD
 
11
BRL
 
10
Other values (15)
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13452
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 4309
96.1%
CNY 92
 
2.1%
EUR 27
 
0.6%
CAD 11
 
0.2%
BRL 10
 
0.2%
GBP 7
 
0.2%
CHF 4
 
0.1%
JPY 4
 
0.1%
AUD 3
 
0.1%
INR 3
 
0.1%
Other values (10) 14
 
0.3%

Length

2024-08-12T20:43:33.033592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd 4309
96.1%
cny 92
 
2.1%
eur 27
 
0.6%
cad 11
 
0.2%
brl 10
 
0.2%
gbp 7
 
0.2%
chf 4
 
0.1%
jpy 4
 
0.1%
aud 3
 
0.1%
inr 3
 
0.1%
Other values (10) 14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
U 4339
32.3%
D 4328
32.2%
S 4312
32.1%
C 107
 
0.8%
Y 99
 
0.7%
N 98
 
0.7%
R 45
 
0.3%
E 29
 
0.2%
B 17
 
0.1%
A 15
 
0.1%
Other values (13) 63
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4339
32.3%
D 4328
32.2%
S 4312
32.1%
C 107
 
0.8%
Y 99
 
0.7%
N 98
 
0.7%
R 45
 
0.3%
E 29
 
0.2%
B 17
 
0.1%
A 15
 
0.1%
Other values (13) 63
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4339
32.3%
D 4328
32.2%
S 4312
32.1%
C 107
 
0.8%
Y 99
 
0.7%
N 98
 
0.7%
R 45
 
0.3%
E 29
 
0.2%
B 17
 
0.1%
A 15
 
0.1%
Other values (13) 63
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4339
32.3%
D 4328
32.2%
S 4312
32.1%
C 107
 
0.8%
Y 99
 
0.7%
N 98
 
0.7%
R 45
 
0.3%
E 29
 
0.2%
B 17
 
0.1%
A 15
 
0.1%
Other values (13) 63
 
0.5%

Total assets
Real number (ℝ)

HIGH CORRELATION 

Distinct4475
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6801947 × 108
Minimum1024
Maximum1.52041 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.2 KiB
2024-08-12T20:43:33.128304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1024
5-th percentile3521482
Q118065700
median50876600
Q31.3168025 × 108
95-th percentile6.0919052 × 108
Maximum1.52041 × 1010
Range1.5204099 × 1010
Interquartile range (IQR)1.1361455 × 108

Descriptive statistics

Standard deviation5.3726484 × 108
Coefficient of variation (CV)3.1976345
Kurtosis225.91437
Mean1.6801947 × 108
Median Absolute Deviation (MAD)40574850
Skewness12.24925
Sum7.533993 × 1011
Variance2.8865351 × 1017
MonotonicityNot monotonic
2024-08-12T20:43:33.234782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132670000 2
 
< 0.1%
100625000 2
 
< 0.1%
144976992 2
 
< 0.1%
46608800 2
 
< 0.1%
37457200 2
 
< 0.1%
28483600 2
 
< 0.1%
116454000 2
 
< 0.1%
14000000 2
 
< 0.1%
69067000 2
 
< 0.1%
25318800 1
 
< 0.1%
Other values (4465) 4465
99.6%
ValueCountFrequency (%)
1024 1
< 0.1%
12443 1
< 0.1%
113809 1
< 0.1%
164495 1
< 0.1%
228025 1
< 0.1%
333008 1
< 0.1%
360600 1
< 0.1%
376141 1
< 0.1%
393449 1
< 0.1%
396368 1
< 0.1%
ValueCountFrequency (%)
1.52041001 × 10101
< 0.1%
1.049559962 × 10101
< 0.1%
8043539968 1
< 0.1%
7759580160 1
< 0.1%
7433039872 1
< 0.1%
7170240000 1
< 0.1%
6478000000 1
< 0.1%
6365200000 1
< 0.1%
5858999808 1
< 0.1%
5666699776 1
< 0.1%

EPS Annual
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4404
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061810526
Minimum-2997.8
Maximum11880.286
Zeros0
Zeros (%)0.0%
Negative2060
Negative (%)45.9%
Memory size35.2 KiB
2024-08-12T20:43:33.344953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2997.8
5-th percentile-9.760935
Q1-1.128025
median0.1537
Q32.28715
95-th percentile10.01468
Maximum11880.286
Range14878.086
Interquartile range (IQR)3.415175

Descriptive statistics

Standard deviation196.60764
Coefficient of variation (CV)3180.8117
Kurtosis2998.539
Mean0.061810526
Median Absolute Deviation (MAD)1.64295
Skewness47.969021
Sum277.1584
Variance38654.565
MonotonicityNot monotonic
2024-08-12T20:43:33.444121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6003 3
 
0.1%
-0.0212 3
 
0.1%
-0.0216 2
 
< 0.1%
2.339 2
 
< 0.1%
0.1243 2
 
< 0.1%
0.895 2
 
< 0.1%
-0.6398 2
 
< 0.1%
-0.3885 2
 
< 0.1%
-0.214 2
 
< 0.1%
0.1052 2
 
< 0.1%
Other values (4394) 4462
99.5%
ValueCountFrequency (%)
-2997.8 1
< 0.1%
-2181 1
< 0.1%
-1952.8796 1
< 0.1%
-1586.1632 1
< 0.1%
-1564.215 1
< 0.1%
-1018.2512 1
< 0.1%
-1008.1846 1
< 0.1%
-471.8591 1
< 0.1%
-434.6842 1
< 0.1%
-355.2372 1
< 0.1%
ValueCountFrequency (%)
11880.2857 1
< 0.1%
2241.184 1
< 0.1%
788.6044 1
< 0.1%
463.3511 1
< 0.1%
201.4798 1
< 0.1%
149.2047 1
< 0.1%
133.0526 1
< 0.1%
117.4103 1
< 0.1%
92.8812 1
< 0.1%
77.818 1
< 0.1%

ROI Annual
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3325
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-69.626028
Minimum-108880
Maximum12633.08
Zeros0
Zeros (%)0.0%
Negative2038
Negative (%)45.5%
Memory size35.2 KiB
2024-08-12T20:43:33.553073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-108880
5-th percentile-157.462
Q1-26.3125
median1.41
Q38.2525
95-th percentile24.3945
Maximum12633.08
Range121513.08
Interquartile range (IQR)34.565

Descriptive statistics

Standard deviation1820.9065
Coefficient of variation (CV)-26.15267
Kurtosis2922.7289
Mean-69.626028
Median Absolute Deviation (MAD)10.525
Skewness-51.212081
Sum-312203.11
Variance3315700.4
MonotonicityNot monotonic
2024-08-12T20:43:33.760639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.15 7
 
0.2%
4.19 7
 
0.2%
4.53 7
 
0.2%
9.32 6
 
0.1%
1.17 6
 
0.1%
3.2 6
 
0.1%
0.64 5
 
0.1%
3.7 5
 
0.1%
3.28 5
 
0.1%
5.37 5
 
0.1%
Other values (3315) 4425
98.7%
ValueCountFrequency (%)
-108880 1
< 0.1%
-41952.85 1
< 0.1%
-29104.88 1
< 0.1%
-9780 1
< 0.1%
-5196.477 1
< 0.1%
-5033.59 1
< 0.1%
-3715.82 1
< 0.1%
-3645.8 1
< 0.1%
-3304.87 1
< 0.1%
-3184.29 1
< 0.1%
ValueCountFrequency (%)
12633.08 1
< 0.1%
2662.67 1
< 0.1%
1875.53 1
< 0.1%
1670 1
< 0.1%
1054.47 1
< 0.1%
737.69 1
< 0.1%
643.41 1
< 0.1%
445.65 1
< 0.1%
432.25 1
< 0.1%
307.73 1
< 0.1%

Interactions

2024-08-12T20:43:26.541673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:11.634305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:12.827781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:14.156754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.310821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:16.539882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:17.706647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.013929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:20.157477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:21.352517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:22.740824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:23.961430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:25.326153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:26.623380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:11.716608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:12.922601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:14.243290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.388330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:16.622901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:17.790693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.093826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:20.237421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:21.445642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:22.831610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:24.053689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:25.416162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:26.712577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:11.810556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:13.019056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:14.333580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.475461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:16.712198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:17.888185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.186919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:20.326536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:21.547583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:22.932590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:24.155310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:25.514169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:26.801830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:11.897395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:13.111789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:14.416402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.554931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:16.797643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:17.975120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.269184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:20.410090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:21.745917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:23.024517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:24.253232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:25.607911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:26.883907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:11.980455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:13.201796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:14.498330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.632545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:16.881552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:18.063245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.352808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:20.493132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:21.836595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:23.108246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:24.347314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:25.698983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:26.974252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:12.073173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:13.304686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:14.599950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.721906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:16.973868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:18.164437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.444730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:20.584057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:21.941117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:23.199873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:24.557862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:25.799952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:27.066655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:12.165663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:13.405819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:14.694132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.809019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:17.068684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:18.271230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.536403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:20.675004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:22.039924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:23.299198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:24.655301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:25.893198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:27.152803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:12.252340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:13.497739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:14.779734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.889832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:17.153784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:18.368788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.623917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:20.758247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:22.135897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:23.388055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:24.750659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:25.984688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:27.341492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:12.345879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:13.593482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:14.868327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.976504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:17.242476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:18.459566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.721452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:20.855125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:22.237097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:23.484207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:24.845695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:26.077648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:27.433042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:12.446467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:13.786361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:14.962472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:16.067241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:17.335109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:18.555847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.812328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:20.960352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:22.340883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:23.580048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:24.942860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:26.178900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:27.522866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:12.542263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:13.879323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.046556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:16.159554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:17.422503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:18.641818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.893874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:21.055270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:22.439555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:23.670922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:25.037250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:26.266104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:27.616385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:12.641309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:13.974560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.140141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:16.257355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:17.521317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:18.834865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:19.985380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:21.157274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:22.541345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:23.771719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:25.135515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:26.363609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:27.713404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:12.740289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:14.067960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:15.229214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:16.461127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:17.614541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:18.924592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:20.072761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:21.259570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:22.644118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:23.870484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:25.234690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-12T20:43:26.456327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-08-12T20:43:33.859336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
52 Weeks High52 Weeks LowBetaCurrencyEPS AnnualExchangeMarket Cap (in M)Performance (52 weeks)PricePrice 52 Weeks AgoROI AnnualRésultat netSectorTotal assetsVolume 1 monthVolume 52 weeks
52 Weeks High1.0000.897-0.0360.0000.5620.0140.7340.3600.9060.9650.5270.5540.0000.2060.2810.228
52 Weeks Low0.8971.000-0.1340.0000.7080.0160.8310.4810.9830.9120.6610.6570.0000.2850.2280.169
Beta-0.036-0.1341.0000.000-0.2480.0920.028-0.070-0.096-0.071-0.270-0.2560.1250.1550.2430.266
Currency0.0000.0000.0001.0000.5610.0000.0000.0000.0000.0000.0000.3520.0550.0000.0000.000
EPS Annual0.5620.708-0.2480.5611.0000.0860.6030.3970.6830.5870.8520.8350.0510.2450.1220.079
Exchange0.0140.0160.0920.0000.0861.0000.0460.0430.0250.0000.0180.0760.4200.0750.0000.000
Market Cap (in M)0.7340.8310.0280.0000.6030.0461.0000.5130.8460.7300.5420.5640.0240.7140.6040.569
Performance (52 weeks)0.3600.481-0.0700.0000.3970.0430.5131.0000.5750.2450.3660.3860.0200.2060.0830.051
Price0.9060.983-0.0960.0000.6830.0250.8460.5751.0000.8930.6340.6360.0000.2920.2490.189
Price 52 Weeks Ago0.9650.912-0.0710.0000.5870.0000.7300.2450.8931.0000.5520.5660.0210.2160.2700.208
ROI Annual0.5270.661-0.2700.0000.8520.0180.5420.3660.6340.5521.0000.7420.0410.1900.0870.045
Résultat net0.5540.657-0.2560.3520.8350.0760.5640.3860.6360.5660.7421.0000.0320.2200.1560.123
Sector0.0000.0000.1250.0550.0510.4200.0240.0200.0000.0210.0410.0321.0000.0380.0000.016
Total assets0.2060.2850.1550.0000.2450.0750.7140.2060.2920.2160.1900.2200.0381.0000.7710.780
Volume 1 month0.2810.2280.2430.0000.1220.0000.6040.0830.2490.2700.0870.1560.0000.7711.0000.942
Volume 52 weeks0.2280.1690.2660.0000.0790.0000.5690.0510.1890.2080.0450.1230.0160.7800.9421.000

Missing values

2024-08-12T20:43:27.861083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T20:43:28.112333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SymbolCompany NamePriceMarket Cap (in M)BetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualROI Annual
0TRNSTranscat Inc116.29001051.5526920.93198950909.83794572982.875000147.11584.4500NASDAQ0.211360US15106000.0IndustrialsIndustrial Distribution96.050003USD2.553100e+071.63405.95
1ACRVAcrivon Therapeutics Inc7.1800217.9911340.711853234007.61660174267.79166712.8503.1900NASDAQ-0.381850US-64118000.0HealthcareBiotechnology11.600000USD1.320950e+08-2.7352-49.83
2COLMColumbia Sportswear Co81.83504781.3574870.628373461903.913043556895.41666787.23066.0100NASDAQ0.098148US227407008.0Consumer CyclicalApparel Manufacturing74.540024USD1.250472e+094.092912.97
3MOVEMovano Inc0.371436.5252241.216491151308.146245248723.3750001.4000.2660NASDAQ-0.715289US-27907000.0HealthcareMedical Devices1.300000USD3.505800e+07-0.6339-837.14
4HCKTHackett Group Inc25.5550711.3564920.40413797656.142292127029.33333327.68020.2300NASDAQ0.090217US34749000.0TechnologyInformation Technology Services23.445827USD5.962300e+071.235727.81
5HCWBHCW Biologics Inc0.598022.315802-0.01708215476.4584987464.3333332.1700.5710NASDAQ-0.707849US-27391652.0HealthcareBiotechnology2.040000USD4.048037e+07-0.6956-126.45
6MEIPMEI Pharma Inc3.390022.786971-1.26500552806.442688239755.4166677.8402.7293NASDAQ-0.375572US26155000.0HealthcareBiotechnology5.421965USD3.602900e+07-4.7808-129.43
7LFUSLittelfuse Inc241.95005987.8587211.125715118391.739130100371.250000275.450212.8000NASDAQ-0.060917US194587008.0TechnologyElectronic Components257.600555USD5.715050e+0810.33727.74
8ITRIItron Inc97.66004423.6650571.489375448229.138340737998.833333112.78056.1300NASDAQ0.392232US187596992.0TechnologyScientific & Technical Instruments70.209999USD8.822170e+082.11465.49
9AOSLAlpha and Omega Semiconductor Ltd34.97001036.4660081.833859202726.280632512560.37500047.45019.3800NASDAQ0.019588US-11081000.0TechnologySemiconductors34.299999USD2.058550e+080.42031.32
SymbolCompany NamePriceMarket Cap (in M)BetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualROI Annual
4474BOXBox Inc27.514002.3695710.4103471.836657e+061.355296e+0630.9723.5650NYSE-0.094119US1.070040e+08TechnologySoftware - Infrastructure30.360001USD144976992.00.868429.88
4475NSCNorfolk Southern Corp239.6254177.2276120.8009471.256250e+061.239665e+06263.66183.0900NYSE0.137450US1.791000e+09IndustrialsRailroads210.738556USD226096000.08.03436.10
4476CPACopa Holdings SA88.123673.4571441.1823493.297429e+053.127565e+05114.0078.1200NYSE-0.051162PA6.713850e+08IndustrialsAirlines92.858147USD30748900.012.779613.29
4477GPORGulfport Energy Corp138.142501.3117090.9882212.022063e+052.470174e+05165.13108.8400NYSE0.212501US7.523250e+08EnergyOil & Gas E&P113.989998USD18107100.077.818051.19
4478BABoeing Co167.91103460.6274121.1499397.051050e+066.611630e+06267.54159.7000NYSE-0.288335US-3.441000e+09IndustrialsAerospace & Defense235.720001USD616166976.0-3.667973.73
4479IVZInvesco Ltd16.167272.5241281.2013634.633188e+064.554791e+0618.2812.4800NYSE0.027753US-3.372000e+08Financial ServicesAsset Management15.724797USD450032000.0-0.2131-0.42
4480FBPFirst BanCorp19.743285.2710250.9282111.117185e+061.213457e+0622.1212.7150NYSE0.347341PR3.108070e+08Financial ServicesBanks - Regional14.663054USD163864992.01.709418.25
4481SNDASonida Senior Living Inc29.36418.1133200.8845681.800198e+042.690000e+0434.266.8900NYSE1.992714US-2.302900e+07HealthcareMedical Care Facilities9.840000USD14240900.0-3.1104-3.80
4482COHRCoherent Corp63.349656.8805423.4231482.326590e+062.209304e+0680.9128.4700NYSE0.403250US-4.145670e+08TechnologyScientific & Technical Instruments45.180000USD152460992.0-1.8859-2.24
4483TWLOTwilio Inc60.419701.8364001.5282492.782786e+062.422213e+0678.1649.8561NYSE-0.024610US-5.943220e+08TechnologySoftware - Infrastructure61.930000USD160600000.0-5.5389-9.46